{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic SEIRS Mean-field Model Demo\n",
    "\n",
    "**This notebook provides a demonstration of the core functionality of the Basic SEIRS Mean-field Model and offers a sandbox for easily changing simulation parameters and scenarios.** \n",
    "For a more thorough walkthrough of the model and use of this package, refer to the README."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and Importing the model code\n",
    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
    "\n",
    "#### Install the package using ```pip```\n",
    "The package can be installed on your machine by entering this in the command line:\n",
    "\n",
    "```sudo pip install seirsplus```\n",
    "\n",
    "Then, the ```models``` module can be imported into your scripts as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seirsplus.models import *\n",
    "import networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Alternatively, manually copy the code to your machine*\n",
    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. In this case, the easiest way to use the module is to place your scripts in the same directory as the module, and import the module as shown here:*\n",
    "```python\n",
    "from models import *\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing the model parameters\n",
    "All model parameter values are set in the call to the ```SEIRSModel``` constructor. The basic SEIR parameters ```beta```, ```sigma```, ```gamma```, and ```initN``` are the only required arguments. All other arguments represent parameters for optional extended model dynamics; these optional parameters take default values that turn off their corresponding dynamics when not provided in the constructor. For clarity and ease of customization in this notebook, all available model parameters are listed below. \n",
    "\n",
    "For more information on parameter meanings, see the README.\n",
    "\n",
    "*The parameter values shown correspond to very rough approximations of parameter values for the COVID-19 epidemic.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIGMA = 1/5.2\n",
    "GAMMA = 1/10\n",
    "MU_I  = 0.002\n",
    "\n",
    "R0    = 2.5\n",
    "BETA  = 1/(1/GAMMA) * R0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SEIRSModel(initN   = 1000000,\n",
    "                   beta    = BETA, \n",
    "                   sigma   = SIGMA, \n",
    "                   gamma   = GAMMA, \n",
    "                   mu_I    = MU_I,\n",
    "                   mu_0    = 0, \n",
    "                   nu      = 0, \n",
    "                   xi      = 0,\n",
    "                   beta_Q  = 0.5*(BETA), \n",
    "                   sigma_Q = SIGMA, \n",
    "                   gamma_Q = GAMMA, \n",
    "                   mu_Q    = MU_I,\n",
    "                   theta_E = 0, \n",
    "                   theta_I = 0, \n",
    "                   psi_E   = 1.0, \n",
    "                   psi_I   = 1.0,\n",
    "                   initI   = 10000, \n",
    "                   initE   = 0, \n",
    "                   initQ_E = 0, \n",
    "                   initQ_I = 0, \n",
    "                   initR   = 0, \n",
    "                   initF   = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkpoints\n",
    "Model parameters can be easily changed during a simulation run using checkpoints. A dictionary holds a list of checkpoint times (```checkpoints['t']```) and lists of new values to assign to various model parameters at each checkpoint time. Any model parameter listed in the model constrcutor can be updated in this way. Only model parameters that are included in the checkpoints dictionary have their values updated at the checkpoint times, all other parameters keep their pre-existing values.\n",
    "\n",
    "*The checkpoints shown here correspond to starting a form of social distancing (transmission rate ```beta``` reduced) and testing at time ```t=20``` (testing rates ```theta_E``` and ```theta_I``` are set to non-zero values) and then stopping social distancing at time ```t=100``` (```beta``` returned to its \"normal\" value; testing params remain non-zero).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints = {'t':       [30, 90], \n",
    "               'beta':    [BETA*0.5, BETA], \n",
    "               'theta_E': [0.02, 0.02], \n",
    "               'theta_I': [0.02, 0.02]\n",
    "              }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Checkpoint: Updating parameters]\n",
      "t = 29.90\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 89.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.run(T=300, checkpoints=checkpoints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the results\n",
    "The ```SEIRSNetworkModel``` class has a ```plot()``` convenience function for plotting simulation results on a matplotlib axis. This function generates a line plot of the frequency of each model state in the population by default, but there are many optional arguments that can be used to customize the plot.\n",
    "\n",
    "The ```SEIRSNetworkModel``` class also has convenience functions for generating a full figure out of model simulation results (optionaly arguments can be provided to customize the plots generated by these functions). \n",
    "- ```figure_basic()``` calls the ```plot()``` function with default parameters to generate a line plot of the frequency of each state in the population.\n",
    "- ```figure_infections()``` calls the ```plot()``` function with default parameters to generate a stacked area plot of the frequency of only the infection states ($E$, $I$, $Q_E$, $Q_I$) in the population.\n",
    "\n",
    "For more information on the built-in plotting functions, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/seirsplus/models.py:406: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,.0%}'.format(y) for y in ax.get_yticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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4EBERiZ96pptAEAQAuK6bcCXSSDQuRERExpR6ppuVwpKMRONCREQkfuqZbgKPPfYYjz32WNJlSIPRuBAREYmfwnQTUGiSkWhciIiIxE890yIiIiIiBzdiz7RmpkVEREREDpPCdBN4+OGHefjhh5MuQxqMxoWIiEj8FKabwNNPP83TTz+ddBnSYDQuRERE4qeeaRERERGRg1PPtIiIiIjIWFKYbgIPPfQQDz30UNJlSIPRuBAREYmfwnQTWL16NatXr066DGkwGhciIiLxU8+0iIiIiMjBqWdaRERERGQsKUw3gfvvv5/7778/6TKkwWhciIiIxE9hugmsW7eOdevWJV2GNBiNCxERkfipZ1pERERE5ODUMy0iIiIiMpYUppvAvffey7333pt0GdJgNC5ERETil0q6AHntNm3alHQJ0oA0LkREROKnnmkRERERkYNTz7SIiIiIyFhSmG4CK1asYMWKFUmXIQ1G40JERCR+6pluAtu2bUu6BGlAGhciIiLxU8+0iIiIiMjBqWdaRERERGQsKUw3gTvvvJM777wz6TKkwWhciIiIxE89001g165dSZcgDUjjQkREJH7qmRYREREROTj1TIuIiIiIjCWF6SZw++23c/vttyddhjQYjQsREZH4qWe6CQwODiZdgjQgjQsREZH4qWdaREREROTg1DMtIiIiIjKWFKabwG233cZtt92WdBnSYDQuRERE4qee6SZQLpeTLkEakMaFiIhI/NQzLSIiIiJycOqZFhEREREZSwrTTeCWW27hlltuSboMaTAaFyIiIvFTz3QT8Dwv6RKkAWlciIiIxE890yIiIiIiB6eeaRERERGRsaQw3QRuvvlmbr755qTLkAajcSEiIhI/hWkRERERkcOknmkRERERkYNTz7SIiIiIyFhSmG4CN954IzfeeGPSZUiD0bgQERGJn9aZbgLpdDrpEqQBaVyIiIjETz3TIiIiIiIHp55pEREREZGxpDDdBH7605/y05/+NOkypMFoXIiIiMRPPdNNIJ/PJ12CNCCNCxERkfipZ1pERERE5ODUMy0iIiIiMpYUppvA8uXLWb58edJlSIPRuBAREYmfeqabQHt7e9IlSAPSuBAREYmfeqZFRERERA5OPdMiIiIiImMptjBtjDnTGHNH/fapxpjNxpg76pf3GmMcY8z/GmMeMMZcUn/efGPMV+OqqVldf/31XH/99UmXIQ1G40JERCR+o+qZNsZMA3J77ltrNxzk+X8GfAgo1h86DfgXa+2X9nvOEmA98BvAVcCtwGeBPx919QJAd3d30iVIA9K4EBERid9Bw7Qx5j+ANwFbiHpFLHDOQT7tBeAdwNX1+6cBxxljLgeeBz4BDAP5+qVojDkXeN5au/3Q/xmT2/nnn590CdKANC5ERETiN5o2jzOA+dbac6y1Z1trDxaksdb+BPD2e+hB4JPW2tcBa4G/stauBjYBXwQ+TxSw/8cY83VjzN8bY0aszRjzcWPMSmPMyp07d46ifBERERGReIwmTK9hvxaPw3SDtfbhPbeBUwGstV+w1r4fWAIsB34T+A6wG7hopANZa79lrV1qrV06derU11hWc7juuuu47rrrki5DGozGhYiISPxG0zN9BPCiMWZN/b4dzez0y/zCGPMH1toHiULynmCNMSYHvBN4F/AVICBqJWk9xNeYtGbMmJF0CdKANC5ERETiN5ow/b4xeJ3fAf7VGOMB24CP7/exTwBfs9ZaY8x3gW8Cg8AVY/C6k8KyZcuSLkEakMaFiIhI/A66aYsxZg7wZeAEYDXwx9ba9fGXdnDatEVERERExslhb9rybaJVOc4F/puop1kayLXXXsu1116bdBnSYDQuRERE4jeaNo+ctfb/6rf/1xjzJ3EWJIduzpw5SZcgDUjjQkREJH6jCdMpY8xJ1tonjTEnEZ0cKA3knHMO9XxQmQw0LkREROI3mjD9h8B/GWNmAZt56cmDIiIiIiKT1kHDtLX2UeD0cahFDtMPf/hDAN73vrFYeEWahcaFiIhI/F41TBtjrrPWvssYs5V9rR2GaJ3pWeNSnYzKUUcdlXQJ0oA0LkREROI3mqXx5lprN+53/3hr7XOxVzYKWhpPRERERMbJiEvjHWhmehEwG/gnY8wn6wdwgH8EFsdQoIiIiIjIhHKgnuku4NeA6cD764+FwH/EXZQcmmuuuQaAD3zgAwlXIo1E40JERCR+rxqmrbV3A3cbY5ZYax8Zx5rkEB177LFJlyANSONCREQkfqNZGm+OMeYfgDRRq0ePtfakeMuSQ3H66VpsRV5J40JERCR+o9lO/G+BvwY2Em0n/nicBYmIiIiITBSjCdNbrbX3AVhrrwK0R3GD+d73vsf3vve9pMuQBqNxISIiEr/RtHlUjTGvA9LGmEuBnphrkkN04oknJl2CNCCNCxERkfiNZp3p2cDxwFbgC8CPrbU/GofaDkrrTIuIiIjIODnkdab3Xwpgz6Ytfz6WFYmIiIiITGQHavP45qs8boELY6hFDtNVV10FwJVXXploHdJYNC5ERETid6B1pl8/noXI4Vu8eHHSJUgD0rgQERGJ30FPQDTGrCOajd5jwFp7anwlyaFSaJKRaFyIiIjEbzSreRxfvzbAacC74ytHDkcQBAC4rptwJdJINC5ERETid9B1pq211fqlYq29B1gyDnXJIbj66qu5+uqrky5DGozGhYiISPxG0+bxD+xr85gFhLFWJIdsyRL9fiOvpHEhIiISv9G0eTy33+3HgZtjqkUO08knn5x0CdKANC5ERETiN5rtxH8MTAHOAqYCpVgrkkPmeR6e5yVdhjQYjQsREZH4jSZM/wCYTjQjfQTw3VgrkkN2zTXXcM011yRdhjQYjQsREZH4jabNo9ta++n67eXGmLvjLEgO3dKlS5MuQRqQxoWIiEj8RhOmnzbGnGutvccYcxLwojEmDRhrbS3m+mQUFi1alHQJ0oA0LkREROI3mjB9HnCpMcYD0vXHVhOt8DE/rsJk9CqVCgC5XC7hSqSRaFyIiIjE76Bh2lp7IoAxZhrQa63V0ngN5kc/+hEAV155ZbKFSEPRuBAREYnfaNaZvgD4L2AA6DLG/Ka19taY65JDcOaZZyZdgjQgjQsREZH4jabN42+BZdbaLcaY2cD1gMJ0A1m4cGHSJUgD0rgQERGJ32iWxgustVsArLWbgUq8JcmhKpVKlEpa/lteSuNCREQkfqMJ04PGmD8wxpxijPkDYHfcRcmhufbaa7n22muTLkMajMaFiIhI/EbT5vFB4LNE7R7PAh+JtSI5ZGeffXbSJUgD0rgQERGJ32hW8xgwxtwL7AKestb2xV+WHIrjjjsu6RKkAWlciIiIxO+gbR7GmP8E3guUgQ8bY74ce1VySIaHhxkeHk66DGkwGhciIiLxG02bx0nW2j1rbH3VGHN/nAXJobvuuusArScsL6VxISIiEr/RhOk1xpijrLXr6hu3bIi7KDk0y5YtS7oEaUAaFyIiIvEbTZg+C3jWGLMBmANUjTFbAWutnRVrdTIqCxYsSLoEaUAaFyIiIvEbzQmIR49HIXL4BgYGAOjo6Ei4EmkkGhciIiLxG80609LgbrjhBm644Yaky5AGo3EhIiISv9G0eUiDe93rXpd0CdKANC5ERETi96oz08aY79avf2v8ypHDMX/+fObPn590GdJgNC5ERETid6CZ6bOMMf8MvNsYc+T+H7DWfibesuRQ9PVF++h0dXUlXIk0Eo0LERGR+B2oZ/pNwBNEm7WsetlFGsjy5ctZvnx50mVIg9G4EBERid+rzkxba9cB64wxdwDtwAnA89bax8anNBmtCy64IOkSpAFpXIiIiMRvNCcgXgF8ALgf+KQx5lpr7RdjrUoOybx585IuQRqQxoWIiEj8RhOm3w8ss9b6xpg0cC+gMN1Aent7Aejp6Um4EmkkGhciIiLxG80608Za6wNYaz3Ai7ckOVQ/+9nP+NnPfpZ0GdJgNC5ERETiN5qZ6RXGmOuAu4FlwD3xliSH6qKLLkq6BGlAGhciIiLxM9bagz/JmDcDC4FnrbU3xl7VKC1dutSuXLky6TJEREREpPmZkR4c1Q6I9QDdMCFaXmrHjh0ATJs2LeFKpJFoXIiIiMRvND3T0uBuuukmbrrppqTLkAajcSEiIhK/g85MG2PmWGs37Xf/OGutNm5pIJdccknSJUgD0rgQERGJ36uGaWPMImA28E/GmD+rP+wC/wAsjr80Ga3Zs2cnXYI0II0LERGR+B1oZroL+DVgOvC++mMh8B9xFyWHZtu2bQDMmDEj4UqkkWhciIiIxO9A24nfDdxtjFlirX1kHGuSQ3TzzTcDcOWVVyZbiDQUjQsREZH4jWY1j25jzE1Abs8D1toL4ytJDtVll12WdAnSgDQuRERE4jeaMP1l4BPAxnhLkcOlP+PLSDQuRERE4jeaML3BWntb7JXIYdu8eTOgE87kpTQuRERE4jeadaZ3GGO+YYz5LWPMx40xH4+9Kjkkt956K7feemvSZUiD0bgQERGJ32hmptfVr/U34wb1pje9KekSpAFpXIiIiMTvoGHaWvs3xpiLgfnA/cDq2KuSQ6LtomUkGhciIiLxG80OiH8PzAEWAlXgz9m37rQ0gI0bo3ND586dm3Al0kg0LkREROI3mp7pZdbaDwPD1tr/Bo6KuSY5RL/85S/55S9/mXQZ0mA0LkREROI3mp7plDEmB1hjjAsEMdckh+gtb3lL0iVIA9K4EBERid9o15l+GJgKPFC/Lw2kp6cn6RKkAWlciIiIxG80JyD+2BhzP9FqHtuttRviL0sOxfr16wGYN29eonVIY9G4EBERid9Be6aNMX8F/I619iHgS8aYT8VflhyKO+64gzvuuCPpMqTBaFyIiIjEz1hrD/wEYx621p623/17rLXnxl7ZKCxdutSuXLky6TIS19fXB0BXV1fClUgj0bgQEREZU2akB0fTMx0aYzLW2poxJs3oVgCRcaSwJCPRuBAREYnfaML014GnjDFPAscD/xRvSXKo1q5dC8D8+fMTrkQaicaFiIhI/Ea7nfi5RDsgvmCt7Y23JDlUd911F6DQJC+lcSEiIhK/0fRM32Wtfd041XNI1DMdGRgYAKCjoyPhSqSRaFyIiIiMqcPumbbGmBuAVUAIYK39zBgWJq+RwpKMRONCREQkfqMJ0/8VexXymqxZswaABQsWJFyJNBKNCxERkfiNJkxfA1wJHAH8CngqzoLk0K1YsQJQaJKX0rgQERGJ32jC9DeALcAlwEPA94A3xVmUHJp3vetdSZcgDUjjQkREJH6jWTP6aGvtXwIVa+1PATViNpjW1lZaW1uTLkMajMaFiIhI/EYTplPGmB6iExHbqJ+EeDDGmDONMXfUby8wxqwwxtxtjPm6McapX/7XGPOAMeaS+vPmG2O+erj/mMlq1apVrFq1KukypMFoXIiIiMRvNGH6s8A9wFLgfuDzB/sEY8yfAf8J5OoP/QvwWWvteUTLilwOLAbWA5cBv7/fa/39qKsXAO677z7uu+++pMuQBqNxISIiEr+D9kxba+80xpwIzAI22oMtTB15AXgHcHX9/mnAnfXbPwfeAHwVyNcvRWPMucDz1trth/ZPkPe85z1JlyANSONCREQkfgedmTbGvAN4Hvhf4Pk9LRkHYq39CeDtf5j9QvgQ0GGtXQ1sAr5INNv9CeB/6m0gf2+MGbE2Y8zHjTErjTErd+7cebBSJoVCoUChUEi6DGkwGhciIiLxG02bx+eAM621S4i2Ff+7w3id/fus24B+AGvtF6y17weWAMuB3wS+A+wGLhrpQNbab1lrl1prl06dOvUwSmk+zz77LM8++2zSZUiD0bgQERGJ32jC9C5r7Q6AegvG4GG8zqPGmAvqt98I3L3nA8aYHPBOovWsC0AAWEDLEIzSAw88wAMPPJB0GdJgNC5ERETiZw7WAl3fSrxA1PN8GjATuAMOvK24MWYe8CNr7VnGmGOBbwMZ4FngN621Qf15nwbuq/dmLwa+SRTYr7DWFg9U29KlS+3KlSsP/q9scpVKBYBcLneQZ8pkonEhIiIypsyID44iTP/6q33MWvvfr7Go10RhWkRERETGyYhhejSreSQamOXgnnoq2uF90aJFCVcijUTjQkREJH6j2U5cGtye2XmFJtmfxoWIiEj8Dtrm0cjU5hHxvGgVwnQ6nXAl0kg0LkRERMbU4bV5SONTWJKRaFyIiIjEbzRL40mDe+KJJ3jiiSeSLkMajMaFiIhI/DQz3QQeeeQRAE4++eSEK5FGonEhIiISP/VMNwHf97GlMvQPYytVTDqN096CM6UDY0Zs75FJIAgCAFzXTbgSERGRpqCe6WbV+/G/ofjTO17xuMlnSc+fQ+6sU8ifeyqF15+B01oY9/okGQrRIiIi8dPM9AQXVqqsnX8Z1akddMyZBSkHAov1PGyxTNg/RLCrH/wAk01TuPhs2j/6DvLLlmjWusk99thjACxevDjROkRERJqEZqabUfXR5zCez+qT5nD+yUtGfE5YqeGt2YD3/IuUbruf4o13kT5uHl1/9hFa33qBQnWTUpgWERGJn8L0BFe573EAzmmd9qrPcXIZsosWkF20gLDqUXnoSWpPrWHHR/+S/pOOoecfPkH+TJ2k1myuvPLKpEsQERFpeloab4Ir3/cYTncn7rQpo3q+k01TWLaE9t98J7llp+I9v4Etb/k9tv/+3xEOFWOuVkRERKS5KExPYNb3qTz0FAM9bTyTDQ7pcx3XJX/2Yto//i7SJ8xn+NqbefH0X6N4230xVSvj7eGHH+bhhx9OugwREZGmpjA9gVWfWoMtltkwp5Pnw/JhHcPJZmh98/m0vvtS8Dy2ve/P6P3MV7C+P8bVynh7+umnefrpp5MuQ0REpKmpZ3oCq9z3GACnt04lm331nunRSB85i7bfeDulm+5m4Ns/oXz/E8y85p9IzZw6BpVKEj784Q8nXYKIiEjT08z0BFa+73GcznZS07vH5HhOJk3rFReSv+gsas+8wMbzfp3KI8+MybFFREREmpHC9ARlw5DKfY/jdLXzdCHkCX9ozI6dW7KQtve/Get5bH7L7zF0wy/H7Ngyfh566CEeeuihpMsQERFpagrTE5S3+kXC/iFS06awLqyyLqiM6fFTM3po+/W34bS3sOPjf83uL353TI8v8Vu9ejWrV69OugwREZGmpp7pCaq84hEAnGldXJ6Np6/ZbW2h/UNvY/iG2+j7p/8i6O2n5x8+oU1eJogPfOADSZcgIiLS9DQzPUGVVzyK09FKaka8JwiadIrWd72B9HHzGPzO9ez4nS9gwzDW1xQRERGZKBSmJyAbhpTveRSnuxOnrYXH/CEeG8Oe6ZczjkPLWy8gc/JxDP/kVrZ9+DNaOm8CuP/++7n//vuTLkNERKSpKUxPQLWnXyDsHyQ1bQrGddgUVNg0xj3TL2eMofCGs8mecRKlX9zD1g99Bhsc2kYxMr7WrVvHunXrki5DRESkqalnegIq37OnXzraQvwtMfVMv5wxhsL5S8ExlG+7j22//hfM+O+/w7juuLy+HJr3ve99SZcgIiLS9DQzPQGVVzyK09VOanpPIq9fOO80smcsovSLe9j2kb9UD7WIiIhMWgrTE4wNAir3PYY7pQOnvQWAR7xBHvEGx7WOwvmnkz3tREo33cX2j/811tpxfX05uHvvvZd777036TJERESamto8Jpjqk88TDhbJnHgMxol+F9oW1hKpJf/60yEMKS6/nZ0dbUz70icTqUNGtmnTpqRLEBERaXoK0xPM3vWlp3ftfexN2WTaPYwx5C86k7BSZeh7/0dqahdTPv2xRGqRV3rPe96TdAkiIiJNT20eE0xlRbQkXjqhfumXM8bQ8qbzSB01m74v/Tf9//mTpEsSERERGTcK0xOI9XzK9z2GO6Ud01rY+/hKb5CV49wzvT/jOLRecRHuzKns+sxXGbrhtsRqkX1WrFjBihUrki5DRESkqSlMTyDVx57Dliq407r39ksD9NoavTaZvuk9TMql7T2X4kxpZ8fv/i2lO1cmWo/Atm3b2LZtW9JliIiINDWF6QlkT7+0O+OlLR6XZXq4LJN824fJpGn7tTdh8lm2ffjPqa1an3RJk9q73vUu3vWudyVdhoiISFNTmJ5ASnc9jNPTSaq+WUsjcgo5Wt9zGTYI2HzFH+Lv3J10SSIiIiKxUZieIMJimcqDT+L2dGFa8i/52IPeAA96AwlV9kqpKR20vuNiwt0DbHnbHxBWqkmXNCndeeed3HnnnUmXISIi0tQUpieI8n2PQ83DndGDMeYlH+u3Pv3WT6iykaXnzKDwxmV4azaw9f1/pl0SE7Br1y527dqVdBkiIiJNTetMTxDlOx6EdIrUzKmv+NgbMt0JVHRw2ROOJuwfonL3I+z8f//MtC9/KumSJpV3vOMdSZcgIiLS9DQzPUGU7niI1LRu3J7OpEs5JLmzTyFzwtEMff9n9H3tmqTLERERERlTCtMTgL9lB96q9bhTu3By2Vd8/H5vgPsbqGd6f8YYCm9cRmrOdHb/7Tcp3nR30iVNGrfffju333570mWIiIg0NYXpCaB0x0MAODNGbucYtj7DDdYzvT/jOLS+8xKcjla2/9ZfU312bdIlTQqDg4MMDia3mY+IiMhkoDA9AZTvXIlpLYzYLw1wcaabixu0b3oPk0nT8u43gIWt7/xj/D6FvLhdfvnlXH755UmXISIi0tQUphucDUNKdzyEO7ULt7Mt6XJek1RnOy1XXEjQ28fWt/8R1mvc2XQRERGR0VCYbnC1J58n3D2AO70bkxp58ZV7vX7u9frHt7DDlD5iJvmLz6b29Bq2//bnky6nqd12223cdtttSZchIiLS1BSmG9yefmn3VfqlASo2pGInzjrOucXHkVl8PMX/u53d//LfSZfTtMrlMuVyOekyREREmprWmW5wpTsewunpIj1j5H5pgAszjbu9+KspXHQm4a5++v7xO2SOP4rWN70u6ZKazlvf+takSxAREWl6mpluYOFwicqDT+D2dGJaC0mXM6aM49D6jotxOlrZ8Vt/oxU+REREZEJSmG5g5btWQs3HnfnKLcT3t8LrZ8UE6Zne354VPqy1WuEjBrfccgu33HJL0mWIiIg0NYXpBla89T5MNkNqzowDPs+3Ft/acapqbKU622m94qJohY93fkIrfIwhz/PwPC/pMkRERJqasRM0hAEsXbrUrly5MukyYmGt5cWT34HJpmm94iJMJp10SbGqPPoc5dvuo+XtFzHjW3+ddDkiIiIiLzdim4BmphtU7ak1BNt6Sc3oafogDZA79XgypxxH8YZf0ve17yddjoiIiMioKEw3qNKt9wHgzJx20OfeVevjrlpf3CXFrnDxWaTmTGf3336L4i33Jl3OhHfzzTdz8803J12GiIhIU1OYblCl2+7Dnd5N+gDrSzebvSt8tLew/aN/Se35F5MuSUREROSA1DPdgILdA6xf+DYyC+dTeOOyA67k0Yz8vgGGvvdT3CkdzF3xPdyOib2NuoiIiDQF9UxPFKVfPQBhSOogS+I1q1RXBy2Xv55g+y62vudPsb5W+BAREZHGpDDdgEq33Y9pyeMeZEm8Pe6o9XFHE/RM7y8zbzb5159O9ZFn2PHH/1/S5UxIN954IzfeeGPSZYiIiDQ1hekGY4OA0i/vx53WjdvVPqrPSRlDqglnsHOnnUhm0QKGf/Rz+r95bdLlTDjpdJp0uvlXghEREUlSKukC5KUqDzxJ2D9E5sSjMSl3VJ+zLN0Zb1EJKlx6LsGuAXb95b+TPu4oWi44PemSJow3vOENSZcgIiLS9DQz3WCKP78bUu5Bdz2cLIzj0PquSzAtebZf+RfU1m1OuiQRERGRvRSmG4i1luJNd5Oa2YM7bcqoP+9Xtd38qrY7xsqS5eSytL77Ddiax5a3/yHhUDHpkiaEn/70p/z0pz9NugwREZGmpjDdQGrPvIC/YSvujKk4ueyoPy9nHHKmud/KVHcnLW+9gGDLTra+78+wQZB0SQ0vn8+Tz+eTLkNERKSpqWe6gRR/fjcYgzt7+iF93jlN3DO9v8zRcwnOW0LlrofZ+akvM+2Lf5p0SQ3t4osvTroEERGRptfc05kTTPGmu3Fn9JCe2ZN0KQ0rf+bJpBfOZ+i/lzPwXzckXY6IiIhMcgrTDcLbuI3ak8/jTp+CaTm0P83fVtvFbbVdMVXWeFreeB7ujB56P/MVyvc8mnQ5DWv58uUsX7486TJERESamsJ0gyj+fAUAqdnTD3nXw1aTotVMno4d4zq0vPMSTD7H1g9+mtqLW5IuqSG1t7fT3j66tcpFRETk8ChMN4jiTXfh9HSSOsR+aYCz0h2cle6IoarG5RZytL7rEmylxtZ3fIJwuJR0SQ3n9a9/Pa9//euTLkNERKSpKUw3gGD3AJX7Hic1rRunvSXpciaM1NQptLzldfgbtrL1A5/ChmHSJYmIiMgkozDdAIo33Q1hiDvn0Fs8AG6p7eKWSdQzvb/MMUeSW3YqlXsfY+envpx0OQ3l+uuv5/rrr0+6DBERkaY2eRptG9jw8l/hdLWTmnt4ux52TqJ+6ZHkzjqFoLefoav+l+yJR9Nx5RVJl9QQuru7ky5BRESk6U3uFNYAgl39lO9+mPSx83A72w7rGGdMsn7plzPG0PKm8xjqG6T3018mfdQcCucvTbqsxJ1//vlJlyAiItL01OaRsOKNd0EQkj5iJsbR23G4jOvS+u43YAp5tn3o01RXrUu6JBEREZkElN4SNrz8VzhTOnDnTDvsY9xc6+XmWu8YVjUxOfkcre+9FBuGbH37H+Hv7Eu6pERdd911XHfddUmXISIi0tQUphPk7+yjvOJR3Bk9uJ2Hvx5wj8nQYzJjWNnElerqoPXtFxPsGmDLFX9AWK4mXVJiZsyYwYwZh9eHLyIiIqOjMJ2g4o13QhiSmjvjNbV4LE23szStzTn2SM+dQeGyZXirX2TrByfvknnLli1j2bJlSZchIiLS1BSmEzT8v7/C6e4kNefQN2qRA8ueeDS5c0+lctfD7Px//5x0OSIiItKkFKYT4m/fReXex0jN6MHtem2zyjdVe7mpqp7pl8udfQrpE45m6Ps/o++r30+6nHF37bXXcu211yZdhoiISFPT0ngJGV5+O1iLe8SMw9qoZX8zHPVLj8QYQ8sblzE8OMzuv/sW6XmzaL38wqTLGjdz5sxJugQREZGmpzCdkOEf/wJ36hQyR8x8zcdaon7pV2Uch9Z3XsLg1f/H9t/5Au70bvJnnZJ0WePinHPOSboEERGRpqc2jwTUnn+R6mPP4c6eimlrSbqcpmcyaVrfcxkmk2brr31Sa1CLiIjImFGYTsDQj28BY0jPm/2aWzwAflbdyc+qO8egsubltrXQ+mtvBD9gy9v+AG9r83+9fvjDH/LDH/4w6TJERESamsL0OLNhyPB1t5CaM53U7LFZxWOOm2OOmxuTYzWz1JQOWt55CeHgMFve/LsEA0NJlxSro446iqOOOirpMkRERJqawvQ4q9z/BP7GbbizpuEUxiYAL061sTjVNibHanbp2dNoeesF+Ju2s+Vtzb2py1lnncVZZ52VdBkiIiJNbVzDtDHmEWPMHfXLd40xHzXG3G+M+Y/9nvMDY0zTnlE39ONfQDZN+qhZSZcyaWUWHEHh0nOoPfMCW9/3SazvJ12SiIiITFDjtpqHMSYHGGvtBfs9didwDnCDMaarfvtua+3geNU1nsJKleL/3U5q9nRS03vG7LjL6/3Sl2enjtkxm132pGMJixUqdz/M9t/+PNO//Tdj0r/eSK655hoAPvCBDyRciYiISPMaz6XxTgEKxphb6q/7GaAEZOr3Q+AjwHvHsaZxVfz53YSDRbKnLsRk0mN23KPUL31Y8medjB0uUVx+O709XUz9xz9OuqQxdeyxxyZdgoiISNMz1trxeSFjTgLOAv4TOAb4OfDrwB8CtxCF6heJQvdc4CvW2lUjHOfjwMcBjjjiiNNefPHFcal/LGx55yeoPr6alndcRKqnK+lyBLDWUvzpnXir1tH5xx+m+zO/mXRJIiIi0phG/BP2ePZMrwa+byOrgV3Ai9ba9wA/Bs4D1gCzgM8BfznSQay137LWLrXWLp06deK0NXjrt1C+62FSc2fgTulIuhypM8bQ8pbXkTpqNv1f/h59/3pN0iWJiIjIBDKeYfojwJcAjDGzgHZga/1jnwb+ESgAAWCB1nGsLXaDP7gRjCE1fzbGGdsv+w3VHdxQ3TGmx5xMjOPQ+vaLcefMYPfnv8HAd29IuqQx8b3vfY/vfe97SZchIiLS1MazZ/o7wFXGmBVEYfkj1lrfGDMP6LTWPm6McYAjgJuAz45jbbGyvs/QD28iNXcG6bmvffvwlzvGLYz5MScb4zq0vfsShn70c3o/9WWcQp62916WdFmvyYknnph0CSIiIk1v3Hqm47B06VK7cuXKpMs4qOIv7mHbBz9NbtkS8mefknQ5cgC25jH0g5sIdvUx/dt/Q+vbXp90SSIiItIYEu+ZnrQGv/8zTGuB1DytLd3oTCZN2/veiNPZzvaP/w3FW+9LuiQRERFpYArTMfO37qR0672k5s4gNa07lte4vrqD69UzPWZMNkPr+96I05pn269/huLtDyZd0mG56qqruOqqq5IuQ0REpKkpTMds8L+XQ2hJHz0X48bz5V7otrDQbYnl2JOVW8jT+v43Y/JZtn3gU5QmYKBevHgxixcvTroMERGRpqae6RjZao0XF78LU8jR8tYLcAraXGWiCYZLDF9zI2G5wszv/yOFC89MuiQRERFJhnqmx9vwT+8g6O0jNW9WrEE6sJZgAv9S1Mjc1gKtH3gzTj7H1g9+mtKvHki6pFELgoAgCJIuQ0REpKkpTMdo4DvX40zpID1/bqyvs7y2k+W1nbG+xmS2N1AX6oH6l/cnXdKoXH311Vx99dVJlyEiItLUFKZjUnnsOaornyZ15Ezcns5YX+sEt4UT1DMdqyhQvwWnkGfrBz9N8dZ7ky7poJYsWcKSJUuSLkNERKSpKUzHZPA712OyadILjsCYEVtsxojluNztTB3qQ50e8XJb8tEMdWuBbR/+DMM/uzPpkg7o5JNP5uSTT066DBERkaamMB0Df2cfw9ffRuqIWaRnT4/1tdK5x6l5HutfeAdbntcJjnFzW6JVPpz2FrZ/9HMM/ujnSZf0qjzPw/O8pMsQERFpagrTMRj8z59gPZ/0grmYdJw7tvsUOn5Mx/RfMW3mrWwbXMTOLW0xvp5AFKjbPvAW3J4udv7B39P/7euSLmlE11xzDddcc03SZYiIiDQ1hekxFhbLDHz3BlJHzIz9xMNsy9246W1s3HQGRy/5Mu2dT7Nh83Fs2zBFLR8xc3JZ2t7/ZtxZU9n1ma+y+4tXJV3SKyxdupSlS5cmXYaIiEhTU5geY0M/uJGwb5D00XPjXVfalMm3L6fW28P8k46hZfZxLF32UfItu9m8YwGrnjqaUikf3+sLJp2i7dfeSOrIWfT903fo/ey/0kjrti9atIhFixYlXYaIiEhTU5geQ9b36f/G/+DOnEr66HhnpfNtP8dxh6gNnkAwZxbVtlNwUjWOP/tf6ZxTo1Rp49lnFrHq6WPo3TmVaiWr2eoYGNel9V2XkD7mSAa+eS07//AfsGGYdFkAVCoVKpVK0mWIiIg0tTgbeied4k/vxN+wjdy5p+J0tMb2Oo67i1zrLVQ2zyZ3zql8/36AVj4890SmhDcyuPTztJ/QRv8DGyn2ZXjxxaOizzMeuVyZbK5GOuORTnmk0z6ptEe6fkmlPGJdfKQJGceh5fLXU/rFPQz96Of4O3cz46q/w8llE63rRz/6EQBXXnllonWIiIg0M4XpMWKtpe/ffxht0hLzcnj5jmvBhgTpk8n1dHLm/OhxZ8rZMPwELUPfYLjrs/RcsoApvkdl9XbKmwbxh2r4Q2kGiwVC04k1I739lpRTI5Wukc155PMVcrkyuXyFfK6M4zbGrGujMcbQctkynNYC5V8+wOY3/y6zfvIV3M7kTgg980xtfS4iIhI3hekxUvrlA9QeX0V26Ym4U7tie51U9hmyhZUUnz+W/KXRGsILZ+/56AyCzDym5K9hOPgEuK04qTSFE+ZQOCF6hg1DbLVGMFQkHKoQDFfxSz5hJSCo+IS1kNAzBCWHYinHgDMDzJ5uoJBcpkhre5HWtiJtbYNkMlp6bX/5ZUswbS2Ub7uPTa//DWb937+RnjsjkVoWLlyYyOuKiIhMJgrTY8BaS98Xv4vT2Ub6uKNinJX2KXT+AL/YinPEkr0nOJaq0UcLWXBnX4C77ipah/+d4Y5PveIIxnEw+RxOPgfTDvxqNvAJ+otUtw/i7SzhDXrUyi67qt309kYBMZsepqNrkM7OAVrbhtQiAuROOQ6nrUDx/+5g04UfYeb1XyF30rHjXkepVAKgUCiM+2vL4bNBQDhcIhwqEQ4OEw4VCQeLhMPRtS2VsTUf63nYqof1PNhzPwgxjgOOAeNgHAOOAwZMJo3J5zD5LM7+14UcTksep7Mdt6sNp7MdU8jFvNmUiEjzUJgeA+XbH6T68DNkTzuB1Izu2F4n1/pLUumtDK09ndYrjtn7+LUPRtdXnge0HEmQmU938G2G/d+CVOdhv55xU6S6O0h1d7zk8aBUpry2l8rmItVBw47t09mxYxYpU6Vzym6m9PTR2jo8qYN1Zv5cnF97I8PX3cKWN/4O06/6W1ouPntca7j22msB9Uw3irBcxd+yHX/jdvzNOwh27CLo7Sfo7cPfuZtgx26C3n7CvkEY7UmsxoDrYFx3b2gGwFqw0bXFQli/BMHojptO4Xa0RgG7uxN3Zg+pGdHF3XvdTWp6D06rflkTkcnNNNJSXodq6dKlduXKlYnWYK1l85t/F2/1ixTefD7p2VNjeR3j9NM54zPUdnbiHPt20nP37ay4amt0fdzM+gPlLfDCt+ktfoiBjn+OpZ79+X1DDD+zk/L2GpWgA2tSpJwy3VN3MXXaLrLZauw1NKpgYIjh/7mZcLBI99/8Hp2/895xe+1Vq1YBcNxxx43ba05mNgzxN+/Ae2Ej3tpNeOs24W/ajr9xG96m7YS7+l/5SZk0Tj4bXWcymHwGk81ismlIpTBpF1IpnEwGcmlMLhvdTrvgpnBSTj1EG/b+9mr2/GdfoI4KtFg/wHo+tuaB5xNWa7BnhrtajWa86x+z1RphzcNWqthKDVuugP/KMO50tJKaO4P0/Dmkj5wVrbG/53ruDEwmHc8XXERk/I04Tagw/RqV7niIre/+E7KnnUD+gtOjP7HGoGXKt8jkHqK49WJaLz34DGewbjnO8BNssD/Hz5wSS00j8YfKDD+xldI2j0rYCcahJbebabN209nZh+NM3PF2uMJqjeGf3EqweQdt73sTU//lk5iU/ig0UYXDJWqr1lFbtT4Kzi9sovbCBvx1m7HV2r4nplM4rQVMNoPTkse05DGFPE4hh9PWgtPRujdIm3Qa4zbuSqU2CAhrHhTLBP3DhIND2OEyYaVKWCpjh0qExTK2WH7prLrrkJo7g8xxR5E5bh6ZY+eRPuYIMsfO04y2iExECtNjzVrLljf/LrXV6ym85XzSsw7ShHyY0tmnaJv6ZYovLCB/0TtwWl+6GctwfSnh1v33iPFLhM/9O5XqUWwt3LrfSYTjp7p9kKHHd1AcSBOYAq6pMnXaDqbN2Ek67Y97PUmyYUjpF/dQe2oN2dMXMfMH/1/sK30MDw8D0Noa3zKNzcz6Pt66zdSeWUvt2ReoPvMCtWdewH9x674nuQ5Oe2vUd9zWEl1aCzhd7dEln8Nk07H9kt1IbBgSVmqEu/sJdg1g+4cIi+Wo73tgmHC49JKg7U6bQmbhfLInH0fmpAVkFx1Dev6cqGVFRKQxKUyPteIv7mHbBz8d76y0qdIx/XPYSg0v92byi1/5J/ur7o6urzzvpY+HvY/hbFvOjvL/Y6jtk2Nf2yiFvs/wU9sYXluiEnZhCOjs2MnM2TvJF8qJ1ZWEykNPUr7rYVIzpzLzJ18mc/QRsb3WVVddBahnejRsEFBb/SLVx56j+uhzVB9/jtrTL+ybaTYGp7NtX1DuaMXtaMOZ2hXNOueyOmHvAKy1hMVK1Ce+Y1cUrgeLhANDhAPDe0O2yWZIH38U2VOOI3vSMWQXHUNm0YLE12wXEalTmB5LNgjYeP6VBL39FN50HukZPbG8Tr7jf8i33cLQ6nNoveKiEQP7mu3R9YLpL/uAtQRrrsWpPM9G+794mdNjqfFQVDb1M/B4L6VSC9akaM3tZtaR22ltnTwrgXhrNzL80zsxKZfp//k3tFxyTiyvs2bNGgAWLFgQy/EnKhuGeOs2UX1sFdVHn42un1iFLdd7+7Np3K6OqA2jsw2ns43UtB6cjhaF5jEWhewywdad+Nt6CfsGCfsGCfqHoFZfdjPlkjn+KHKnLyJ32olklywkffTcSTHbLyINR2F6LA3+4EZ2/tE/kjvnFHLnnBrL/2Dd9Iu0T/sClQ1zSZ9xBanDWb86KBM8+00Cr8Cm7K1YJ57Qf6i8wTIDD21heHeG0OQoZPqYOXcbHZ2TI1T7uwYo/uQWwoFhOv/4Q0z59McUDmISlipUH32WyoNPUnnoKSoPPkU4MBR9MJ3C7WrH6WzDndKJ092BO6MHt7WgE+cSFNY8gu278Osnbga7Bgh29+89AdK0Fsiechy5M04id9oJ5E5fhDul48AHFRF57RSmx0pYrrLhrPeBtRTeeB6p7s4YXiWgfdrfYsKdVKuXUjj31U8iHIiWE6bjVc7nscMbYd33KHknsi3/f2Aa50+mQcVj4KHNDG1zCEyeXGqAmUdso6troOlDdVjzKC6/HX/9ZvLnncb0734Bt2Ps+qgHBgYA6OiYXCHD39YbBef6pfrk83tDmDOlA7ezDaenC2dKO6mZU3HbWxWcJ4CwWsPfvAN/41aC3QOEvf1Ri0j9/2Hpo+eSO3cx+bNOIXfWKYltliQiTU1heqz0/dsP2P03Xyd/welkl54Yy6x0rvXnFDqvY2jVElovf+MBV394tZ7p/QU7n8Tdfj0D1cvoLfxXIickHkjo+Qys3MrQphDfFMi6Q8yYu43u7r6mDtXWWir3PkblvsdxZ/Qw84f/H9kTx6YtYzL0TFtr8ddtpnzPo5Tve4zK/U/gb9wWfTDlRmskT+nEmdoZrY3c06UNSZqEtZZwqIi3fgvBlp0EvX0EvX3gRSc3u9O7yZ19CvlzFpM76xQyx83TX39E5LVSmB4L/s4+Npz5PtyudlredB5OW8uYv4ab2kz79M9T3TaV1MIrSM058Coha3dE1/MPspiIv/EuUgO30197B7vy/9ZwgRog9AMGH93K4Is+Pi1k3SFmHrGVKVP6mzpUe+s2UfzZXdgwZOoX/5T2973pNR9z7dq1AMyfP/81H6tRWGvxX9wahed7HqG84lGCrTsBMC153CkduFO7cLo7SM2ajtvZplnnSSSs1fA3bMV/cWsUrnf27e2FdzpayZ17KoULTid/3mlR33Uz/1ARkTgoTI+FHX/0jwz9z80U3nA22UXHHPwTDplP+7S/x2Eb5f5LaLnwtLE7tLX4639FqriC/to72ZX/14YM1ABhEDD42DYG13n4tJBzB5l55Nambv8IBocZvv42wp19tFxxIdO+8mmclvzBP7HJeRu3UV7xCJV7HqW84hH8zdFvj6aQx+3pxJ02JbrMnIrb0aql1WSvMAgItuzEW7cp2mFy+y5sKVpL1J3aRf6808ifvzQK12oLEZGDU5h+rSqPPMPmS3+L9IlH03LRWZhsZsxfI9f2Uwod/8vQs0toveIyTPrgs2p9xei6azST5Nbirf8V6eIKBr2L6c19C2sad/OEMAgYfHQbg+t9fArkUgPMOnIrnZ2DTRmqbRhSuvU+ak+sJjV3BtO/9w/kFh1e20dfXx8AXV2HceJqgvzN2ymveDSafV7xyN62DZPP4vZ0RcF5+hTc2dOjfmf96V5GaW+4fmEDwfZ6uK4vf5iaMz0K1ucvJX/uElLTpiRcrYg0IIXp18KGIZvf9DvUVr9Iy2Xnkp43e8xfI1q942+pbplFatHbSI1ya/LR9Ey/nLfhXlIDt1HxT2R77ocEJp5t0MdK6AcMPLyVwQ0hgcmTT/cz68gtdHQMN2Worj2/ntLN92D9gO7P/z4dH3vnIf9JeqL0TPtbd1Je8Ug9PD+K/+IWYE947sSZNgV3Wjep2dOjmWeFZxkjoR9EbSHrNuFv302wY9fenuv0cfMoXHw2hQvPIH/mybFMnojIhKMw/VoM/vAmdv7hP5A9YxH5ZafFsPWvR/u0L2DC3VRKb6DldYtH/Znre6PreYe46p237WncHcsJbBfb09+i6px1aAdIQOgHDDy0hcFNlsDkKWT7mDNvG21tQ0mXNuaC4SLF//0VwdZe8hedxfSvfw63q33Un79+/XoA5s2bF0+Bh8nfsZvyPY9QWRHNPHtrNwFgclF4dqfW2zbmTMftaGvobbaluYS1Gv66zdFJjdt6oxMaQ4vJZsiddTKFS86h8PrTSR9zpPqtRSYnhenDFezqZ8M5H8RkMxTeuCyWpfD2bs6y6nRaL3/DAVfvGEvBwGbC9TeQcvrYZT/FQPoPGraPen9hzaf/gc0MbnMITY7W3C5mz9tGa2sx6dLGlLWW8l0rqT70NE5nG9O+/jlaLmr8X3r2F+zqp3zvY1HrxoqH8Va/CES73blTu3DqrRup2dNxO1vHbeyLHEw4VKS2ej3+xu3423qxQ9HPF3d6N4WLzqRw4Vnkz1+K2zl2S1qKSENTmD5c23/vbxn+ya3kLzqb3CnHjvnx09knaZv6FUrr55E5822HvDlLb31Stucwf55br0xt1U/J8ixF/0x6s/+Kb+Lb5nosBRWPvvs3MbwzTWiytBV2MefIrRRaSkmXNqa8Tdso3XgX4WCRtl97Iz3/+McHPTmxtzf6k0VPz/hu1BMMDFG573HKdz9CecUj1J55IfpAJo07rStarm5aN6lZU3E727XahkwI1lqCrb3Unl9PsGUn/vZ6S4hjyJx0LIWLz6blojPJLlmok2BFmpfC9OEo3f4gW9/z/8gsWkDhwjPHvG/OOAN0TP8rgqKDn72U/GkLD/kYh9Mz/QrWUtuwEnfgV4Bhl/M5htwrJ8QsNUBQrtF370aGd+cITYaO1p3MPnIb+Xw56dLGTOh5lG65F++ZtbgzpzL9239N/syTX/X549UzHfQPUXnwScr3Rj3PtSefhzCMdhecOgW3u4PU9G6cmVNJdbWr91SaQljz8NZsqLeE7CTcPQjW4rS3kH/9GbRcei6F15+B2zOxTgAWkQNSmD5UYbHMxtf9OmGxTOHSs0nPHuulk0Laer5CKv0cw+vPo/Wt5x3WyVUbd0XXc7tfe0VBsQ//hZvIOmso+afTm/1nPHP8az/wOPGHKvTdt4nhgQLWpOhsi0J1LldJurQxU1uzgdIv7sGWq7R//F10/8Vv4eRfuavlxo0bAZg7d+6Yvr6/eTvl+5+g8sATlO9/HO/ZddEHXAd32hScKZ2kpteXqpvSgZNrnB03ReJgrSXoG8RbtQ5/8w6CrTuxlRoYE03EXHouLZecTXbx8TqBVmRiU5g+VL2f/RoD3/xxbDsd5lpvptD5Y4afW0ThjW9qnDWFraW6fiWpwTtxnAr9fIS+1KewpjXpykbN6y/Tf/8mhodascalq2MHs4/YRjZbTbq0MRFWa5RuvAvvhY24s6cx7d8/S+HcU8f8daznU1u1nsrKp6g88ASV+57A37wdAJNNR/3O9Y1S3Bk9uN2dmGxGJ2fJpBb6Af66jXhrNuFv3Um4qx8Ap7ON/IVn0vKGc6JZ6ykdyRYqIodKYfpQlO5aydZ3/vHe5ZGcQm5Mj5/KPE/b1P+P6rbpuMe8lfS8mYd9rB2D0fW00S/0MCphpUh1zR3k7MP4YTe7U59l2Hk3mInTD+jtLtJ3/2aKxXasMXR3bWfW3O1kMrWkSxsTtefWUfrVA9himdb3XkbP3/0hbkfUPL9jR7S5ybRpB9kas87WPGrPrqX6xCqqT6ym+vhqak+vwdY8oL7DYHdnFKB7OknN6I56ntW2IXJA/u4BvOfW4W/eTrC1N1rb2jFkTz6WwqXLaLnkbDInHaNZa5HGpzA9WsHAEBtf9+vYqkfhkrPHfGcs4/TTMf3zhJWAmn8JhXNfve91NMakZ/oAvJ0vEm7+JdnURirBMexO/xVlcxETaYHn2s4h+h7YRrHchjHQPWU7s+ZsJ53xki7tNQtrHqVb78V7di1OZztTv/RJWt96wQF7psNKtR6cV1N9fBXVx1dRe3bt3jV2TS6L092B09qCO6Udp7sDd3oPbnuLVtsQeQ1Cz8dfu5HaC5sItu4k3D0AgNPVHp3EeMnZ5F9/hlYIEWlMCtOjtf23P8/wDb8k//rTyZ66cIz/ZO3TNvWLpFLrGF6/jNa3nPea19HdHG10x+wYz3OxQUh141O4/StIp3ZSDM6iL/PXVM3i+F40BrXtg+x+cDulSjvGWLqnbGfmnO1kmiBUey9upXTLPYT9Q+QvOJ3wzz6MmTWVmVN6qD2zhurj9eD8xGpqz60FPwDAFHJRe0ZrIQrOne043Z24bS2YfFYtGyIxsdYS7B7Ae3Yt/pYd+Ft7oeZFs9aLj6dw2TJaLj6bzKIF+j4UaQwK06MxdMNt7Pj435A56VjyFywd85OnCh0/JNd2G0PPLqblzZeOeftI3GzgU3nhIdLle0m5wwwFb6A/8ylq5sSkSzsk1W2D9D20L1RP6drBzDnbyWYndvtH6AeU71pJ7bFV4Dqk58/Be/5FCEKgHpyndGDaWqKTAzvbcKZ07AvO+jOzSGLCmof3wga8dZsJtuwk7It6+JyeTlouPpvCJWeTv+B03PaJc/6KSJNRmD6Y2gsb2XTxx3DaWshffBbpGWO7Pm+msILWKd+ltO4oMqe/mdSMMVh+A9jWH13P6ByTw41KWC1TWXs/WW8lrlNiKLyU/vSnqZlDX9ovSdXtg/Q9tI1SuR2MYUrnDmbN2U42N7FPVAx2DdD3q/uhWiM/pROnqx1nSnvU89xa0EmCIg3OWkvQ2xf1Wm/Zib9tz6y1Q3bJwmjpvQvPjGat9UuwyHhRmD6QsFxl8xt/C2/9FvKvP4Pswvljctw9UtnnaOv5ErXebuy0y8idOHbHj7tn+kCCconq2gfIBg/hOmWGwjfSn/6zCReqazsG6XtwG8V6qO7q2Mmsudsn9JJ611d3YK3lnbnpSZciIq9RWKvhPb8Bf/2WaIWQPbPWUzooXHxWdDn/dK0QIhIvhekD2fHH/8TQ939G7txTyZ150pjuYOWkttI+9e8JSylqwYUUlp0yZseGZGamXy4oFamufZBs+CCOqTJsL2Eg/QmqZklyRR2G2s4h+h7cSqnUhjUune1RqJ6Im7/sDKOWlamOVtsQaSbWWoKdu6mtqu/GuG2/XuuTj6PwhnMoXHhGtK61dmMUGUsK069m6H9uZsfv/x2ZRQvIv27pmK73bJwh2qf+HYRDlLadR+sbz2rqP8kFpSLVdQ+S9R/GdYuUwjMYSP0RJXPhhFr9w+sdpu+hLRSHo1Dd3tLLzLk7aW0dTro0EZGXCKs1vBc24r9Yn7XeVV8hpKM12o3xkrPJX3AGqWlTEq5UZMJTmB5J5ZFn2PK238edOoX8hWeQmj6GfdKmTPvUL+G6Gxh64Wza3vo6TDo9dsevG4/VPA5VWKlQWfco6epK0qndVIJj6U/9AUXnCjBj/zWIi9dfpv+BzRQH84QmQyHbz8w5O+jo7G/43w22h1Hf93RHOxCKTBbWWoJd/XtnrYNt9d0YgcwJR1O49BwKF55FbukJWuZS5NApTL+cv2UHmy75Tazvk3/dUjLHHDmG1XnRVuHZVQw/dxotb74otpU7kuyZPpjQ96msfQK3+DDZ9Ba8YCYDqY8x5HyA0HQmXd6oBaUq/Q9uYniHS+C0kHGLTJ+1nZ6pu3Ccxvweur4abdryjuzoNm0RkeYTeh7+C5vx1m/C39ZL2NsP1mJaCxTOX0rh4rMpXHgGqVn6OSEyCgrT+wuLZTa/9ffwnt9A7nWnkT352DFc3SCgtfs/yOQfY+jZUyi84WLcjviWMoprB8SxZIOQ6qbnYPej5NJrCG2OQfNeBt3fxDMLki5v1ELPY/DhzQxv9KmZDlxTZeq0HUyb3ttwG8DsqvdMd6tnWkSIZq3DvkFqq+u91lt2YsvRSdbp+XPIv/4MCq8/g/w5i3HaWhKuVqQhKUzvYcOQ7R/9S4o33kXu3MXkli7CpMfqz10hLV3/RbblPoafO5H8BRfj9nSO0bGbQ3X7Jvytj5N3H8MxPsXwfAZSv03ZXDBh+qrDIKD07A4GVw9RCbuAkM72XqbP6qWlZXii/DNEZBIL/QD/xS14azcRbO8l2NEHQRAtv3fq8RQuPJP8+UvJLTlhDP8fKTKhKUxD9Jt57ye/xOB/Lyd76vHkzl2Ckx+rntKQlq6ryLbcQ/H5Y8me/YYxW0v6QDbuiq7nxv9SY8obHKS24XGy/sOkUgNUg/kMpD7OsPNOrJk4W+lWNvYx8MSO+gogafKZQabP6qVrSrItIFuDqGd6pque6UZiQ0MQugSBQxi6hKHBWmfvtQ0N4UuuHawFiwFrsPDS6z23iX7KG2PBWAw2+qXOWIyx0f8BjMUxFuOEOE6IY8L6bbvf7RDHWBw3wHFC/WI4iYTFMrU1G/A3bifY3hstv2ctppAjf86p0cz1BUtJH3Ok1qmXyUph2lrLrs/+KwPf+jGZk44hd/YpuB1jFdqC+oz0/RTXHEv2jEtIzRrbTV9eTSP3TI9G6HlU1j+FM/wYufQGgjDPsHMFQ+6vT6jtyv2hMgMPb6G4w+A7bbimSs+0nUybvjOR7crVMx2PMDT4foogSOH7LkGQIvBT+IG79zoMXILAjW77LoHvRAE6TGHtGC5VZi0QXcye/5qxXC3I4hgf1w1wnQA3tecSRvfdADflk9pzcaPrPY816vkEcnDWWsLdA9RWv0iwbSf+tl3Y4RIA7rQp5C84ncIFp5M/7zRSY7zBmUgDm9xh2lrL7r/9Jv1fu4bMSceQXXoiqZ6xWv7Cp2XKf5ItPERx9XFkz7yI1OypY3Tsg+sdiq57Js5k7ohsEFLdvpFgx1PknSdxnCqV8EQG3Q/XZ6snxha6oe8z/PQ2hl8oUgk6wUBH2y6mzdxFW9vguM309YVRgO9yJs7qKUmwFnw/jeel8LwMvle/7WfwvDS+l6pfR8HZcoAwbC0OHsZ6OPWLsT6OCTCOxXEsxrUYFxzXwaQMOA4mlcK4BuM6kDIYx8FJRfeNayDlgGMwrhs9x4BxnHpufvmAstgw+n6yEG0lH4SEQQhhdD/0bf0xi/Vt9NwgxPoBtv58G1jCwGDD6BcIGzqE1sXiEpo0oUljTQZrXv3r4Rgf1/GioJ0O6heP9EsuNdLp6Dma7GxcNgzxN+3AW7uRYFsvwY5d2Gr0MyY9fw75151G/twl5M5ZrCX4pJlN7jC9+4vfpe+f/ovMSceQWXz8GG4VXqO1+1tk8o8yvPp4cmdfRGqmfkt/rfzhEtUNT5GqPEk2s4kgLDDsvJ0h9wNUOXXC9FZXN9VbQIothCZL2i3RM72XqT27Gu6ExWYUhgavlqHmZajVMtHtPferabxaBj9IM9LPR2N9XFvGCSu4YRXHCXBci0kRBd2Mi5N2cbIp3FwKN5/G5DM42TQml422bM+lMW5z9ZraMISaj63VCCs1gnKVoOgRlj3CSkBYDQhrPmEtCuuhbwhDh9CmCE2GwMlhzUgnxYak3ShYp7MemYxHOlMjs98lna5ptrtBhDUPf91mvBe3EOzYTbCzD3wfqIfr85fuC9dTG2jdVpHXZvKG6b5/+wG7/+brZBYdQ+bkY0nPHps/exszTGvPv5LKrKG4+gRyyy4iNX38fyNf3xtdz2vCDG+DkOqWDQS9z5B3H8dxalTD+Qy5v8aw824CMzPpEkclKFcZemQzxa0+VaITFttbdzNtxi7aOwZi+d1gcxCdpT/bjWdJxqSFodkXkF8SltPUqtH9IHxlaHNsDTcsRRdTw01ZnIzByaZx8mlSLWmc1iyptjymkIuWtEylmnqzpfGwJ4SH5QrBcBl/sIo/XCMsegSVKHwHPlGLDFkCJ09oXtnvn3KqpNNVMjmPbHa/wJ2uB+5MbaL8rt1UwkoVf+0mvE3bCHb0EfT2gVcP10fPjWauly0hf85i3DH7q7DIuJucYXrg29fR+5mvkjnhaDInHUtq7vQxOXHCcXfT2v0vuKntDD93CoWLz8ed0vGaj3s4JnrP9GgFpRKVDc/glFaRz6zBWoeSXcZw6n0UzWVYM3Y7V8apvG4nQ8/simarnRwpp0L31F66e3aTz1fG7HUmcs/0qwXlai2Nd8CgXI1Csq3gOj5uxuJmHJx8GrctQ6otS6qjgNOSxxTy2mq5QdnAJxwuEwyW8PvKeEO1aPa7GhDUIAhcApvFdwrYV2wCZUm51Shg52rksjUy2SrZbJVstkJGYXtchKUK3rpN+Ju37zdzHQCQXnAE+fOWkDvrFPJnnkRq9vSEqxUZtckXpgev/j92/sk/kz7+qGhG+oiZYxKk3fQGWru/irFFhtecRutlyxJdk7OvGF13TZJlQW1oqfXuxNv6LFn7JOnULoKwlWHnbQy7v0aFpWN8ElY8wkqVoUe3UNxSoxJ2gnHIZQbpnrqb7u6+19wGMhBGs0IdTmO1GQSBg+dFM8jey9sv6jPLQXCAoBxWcN0oKDu5FG4hQ6o1TaqzEAXltgImo7W1m50NQ2y5gt9XxOsr4w/WCIpVgnJA4NUDN3l8p+VlPw9CMqlKFLRzNbLZ6kvCtusGCtsx2BuuN9XDdW//3rYQd0YPubNPIX/WKeTOPInM8UfpF11pVJMrTA/9+Bfs+L2/I3303KhHet6sMQnSmfwDtHR9l7CaprTldFovPQeT0/+4kxL6IdWNLxD2rSafik5a9MIZDDuXU3SvoMriCdFfXds2wNCT2yn1GTynE7C0Fvrpmbabzq4+XDdMusSD2nMi356QHM0qp/fOLnu1NLVahjB8ZbjfF5RLpIwXBeWMg9uSwWnLkurIk+pojYJyNqOWCxkVW6sR9A9T21XE76viD1cJyj5BzeAHGXwn+uvQ/hzjkc1UyOSjWe09ITubq5LJVCfCj5MJIaxUozWuN20n7O0n2Nm3dwMZ01ogt/RE8mcvJnfmSWRPXRjbDsIih2jyhOnh5bez/eN/TerIWWSXLCR91OwxCNIh+fbrybf/nNqubjz/bAoXnBqdWZ+wtdFf85k/8f6aP6aCUonKxlWY4bXkMs/hGJ9aOJeieznDzhXUOLHhg7UNQ8ov7KC4uo9SMU/gtGAIaG/rY0rPAB2dow/WG+o900eMQc90GDivCMaet+f2nlnmNPCy7wcb4lLBDcu4QQnXeLjpqEfZbcngtORId+RwOwo4bS1qvZBxE81sV/F3D+LtKuMNVAiKHkElxPdcfHL4Tivst1qJISSdrpDLVcjla2RzFbLZKrlcRUH7NQo9j2DrTrwXtxL29hHs7CMcGI4+mHLJnLggmrk+7QSyp51Aau4MrXUtSZgcYbp48wq2/cZnSc2aRvb0E0kfNec1f8MZZ5iWrv8kk3+S0vojcWafQ+7UY1/TMcfSZOmZPhTewDDVLatxSi+QzzyHMSG18EiK7pspOm+iypKGbwUJfY/iM9sprR+iUmkhcPIYAtra+pnS00/nQYL1aHumrYVaNUu1lsGrZfdrt6iH5FqGIHzl8nrGeqRsuT6jXMZ1fJw0uFmDk8/gtuZw23M4rVFQdloK2kVNJgzr+QSDw3i9Q3h9FfzBqIXErzn4YRbfaX1Zv3ZIJl0hl6uSy1f3Be1shUxWQftQ2TAk2NWPv27z3p7roG8gWuoRcLo7yJ12IrmlJ5I97QRypy7UFugyHpo/TJd+9QBbP/TnuFO7yJ15Mun5rz1IpzKraJnyLRxnkOLzJ5A98zzScxprCnggWkefjkKydTSqWv8Q3pZVmPIL5DPPY0yAH/ZQdC6l5LyJklkGI6wa0EjCWo3i09sobxymXGkdVbAeqvdMt+3XMx0EDsViC8XhNkrlApVyjmolh33ZjLJjK6T2hGRTi2aT0+Dm0ziFDKm2DG5bHtPSEp3Ml8+q9UImDRuGhENFvN5hvN4i3kCNoOzhV+tB2331oJ3NV8ll94Tt6IRILfd3cNZawlKFYMM2/C07CPoGCHf1Ew7WTxoyhvTRc8iefhK5004gt+QEMguPwqT0C7yMqeYO0+V7HmXre/8Up6ud3DmLxyBIh+Tafka+/f8ISi2Uty6h5eKzcFonxooRMjJvoEh16/NQ3EA+/SyuUyGwBUrmwnqwvojQJLMqy2iF1SqlZ7ZR2likXGkhcApASGthgK6eATo6+slma3ufX6tl6NvdRV9fF8ViG3t+FqTCIdLBICm3SioXtV24bXlSnTnc9nrbRT6nkCxyCGwQEAwV8XuHqO0q4w9UCUo+fs3gh7kRgrYlk4p6snP5+omQ9aCdzVYVtA/AhiHhrn689VsJdvUR9PYT7h7AVqOffyaXIXPiArKLjyd7ynFkTz6WzHHzFLDltWjeMF158Em2vPtPcFry5M49lfSCI15TkHbc3bR0/Sfp3Coqm+YQtp1B/qwTG6I/eiRrtkfXC7S60CHxSzWqm9cSDm0g5zxDOjWAtSnK9jTKqYspmYuosbCh+6zDWo3is9spbxiiUs7hO9E2mLn0EHTtJqwVqPX3AIZ00E/O7iTTmSY7q5PUjG7c7vam21QkNtYCHoYahkr9uoahWr/UMHa/27z8tochoL4NIYYQbHR/3+Mh0c9qp34x9Y3CnXpbkqn/FcGpP++l9y0ulmz9ksGSBTKEZLEmeiy6n3vZ7cJLeoMlHi8P2sFgBb9YPyEyzOK7bYQv2dDGkk5Vo6Cd23ci5J6gPRFOTB5vYbUW9V5v3E7YN0C4a4Cgf3DvsnwmmyZz/HyySxaSPbkesI8/CpPRTrEyKs0Zpu/57g/ZcsUfQDpF7twlZI498jUEaUumcA+Fzh9irB+1dZxxNukjZoxp3WNNPdOvXVgLqWzdQNj3IqlwLbnMBgC8cDol50LKzoWUzfmEpj3hSl+dDQLKL+ygtK6f6oCh4kwhdDy6KuspzMmTOe4I3KlTJt9Ms/VwGMBlAIchjB3GYRiHofr1MI6N7pu994dGeG4JY+L5eWmtE4ViW//ZZWwUtgFjxicwhTZLSAFLCyEFQlqwpuWl1xQITRsh7YS0E9BBSBuh6dj7WEgbGP2CdqhsENRbR4bwdpXwByoEJW9fj7bbSmheejJxyt0vaO/Xo53NVnFTQUL/ksZirY02CtrSS7B1J0H/IOHuAYK+wb2bypBOkTluHtlTF5I9+ViyJxxNZuF89WDLSJovTL/llNPtv9VmYj2P3HmnRb9dHmaQNk4fLV3/TSb/JLVdPdTKp1K4YCnOBFj2bri+z0erVg4aEza0VHv78Xaux5Q3kU8/h+uWsNalYhdTdpdRMedSMUuxpnEb1Qe3bCUcGqJj/lGY9MSfdTG2jMNuXHbh2l249OHQHwVlG13vu92PY+sB2hQPemxrHcIwTxDmCcM8YZgjtNFl30xvOgqJxo1mievXxqnPGjsG4xqMcTHGBTcFrosxKXBccB2M40Y/oxwnujZm38+sA/3o2vNz2lrAYrHRbWujh0KLtRYbBhgbYMMAQr9+vf/tEBtasEH9OoQwBOth8MDW6jPo9dl0U8MxVRynguNE165z8I2FAlsgtO2Eph6wTee+23QQmnYCphDQTWCi65AphLQ39F+CkmIDn3CgiLdrAG9Xab8ZbQffRidDRu1e+7hurT6b/bKgnaviuv6k/jJbawkrVcJtvXhbdhL27Rewa/vW90/NmU5m0QKyJy6INn474ehodTCtODSZNV+Yvm/GuXZ6roX8eUvILDrmMIO0JVO4tz4b7VF8/ljSJ55OduG8sS5XJqig7FHdtolgYCOpYAO5zDqMCbE2RYUllJ1zKZtzqZrTJswujImztj7bu3847t17+yWP215cduOY0qseLgyzBGErQVAgCFoIbYGQPNbmsCaPdTIYN4NxXYzr4qRSmEwGk8pg0jmcdCraUjwVXUzKYJxJnDb2YwNL6EcX60NYCwi8Cng1rF8Bv0oYVMEPsIEHYS0K5baKsRUcU8ExJRynjOuWcJ3iq862W5sioJOAKYRmT9jujsK2mVIP4FPqj/UQ0N3wJw/HzXo+4eAw3u5B/F0l/MEKQdEnqIFX79EOTOElv6Q4jhedBJmPWkZye9fSrpBKTd6gHVZr0cohW3YQ9A8R9g8RDgxFJznWs5LJZkgfeyTZk44hc8ICsiceTfq4o3B7OrVU3+TQfGH6iann2p6LzyF7ynGH9afraDb6ajL5x6nt7qY2uJjCBadNuJMMV22Nro+bmWwdk4ENLd5gidr2Tdjh7aR4kVxmPcaEhDZDlVOoOGdSMWdQMUsJzZTEat00tB6AOW3zxu01jS3jsjO62B2k2IFrd+Cyc99tuwOXHTimNuIxwjCLH7QTBK34QRuhbcVSiP4K4GYhlcFJpzHpLE46i8kWcDNutHZ1Bpy0g3H1P7VGYsN6IPcsYTUkqNWw1RK2VsZ6JazvYX0PwirYMo4t4pgirjNMyh3CdYdfNYAHto3AdhOYqfVLTxS0TQ8B+92nh5DOSTXzvWdGO+gbjE6GHCxHa2lXiTatcVvxzUt3iHSMv7c3++V92um0N5m+fADYIFq5xd+yk6B3N2H/MGH/IOHAELay72eY09FK+th5ZI4/isyxR5I5dh7pY+eRmj1NIbu5NF+YPmP2Ufamj/zJYfQ11WejO36IoUbxhWNJLzyd7MKjYqkzbuqZTo4NLbW+Ml7vi9jidlLhJrLZF3FM1ItXDY+m4p5J1ZxOxZyBx/xx+5/5LS8uB+ANR17+2g9mLQ4DpNhCym6uX28hRXTbtdujkGyGRvhUQxC24/udeH4HYdhGaFqxTgHcDE4qg8mkcLJ5TKaAm8viZky0XnVaM8STWeiHBFVLUAkJqxXCWj2A18pYvwZBFcISDiUchnGdQVKpQVxneMT+9mjmewo+UwnMtOiy5zZT649PJWBa0wdvG4bY4RJ+3xD+7iJeX4WgVMOvWHw/jW9a6pvW7AvahiDaHTJXJZvzyGairdgzmSrZbG1StY/Ymoff20+wdQdh3xDB0DDhwDB2qPiSkG3yWdLHHEnmuHrIPm4e6WOPJH3ELK27PzE1X5hePGeeve0jf4rTOvq+VePupqXze/Xe6G5qlVMpnHfahN6qtFSNrguT+6+dDSGaua7i7dpMMLgTx99KLvMCKTfq2/XtFCrmdKrOGVRYTM2cFNtJjRW/DEAuNfq/tLh2J2m7mjTryNgXSLOOlF1L2m56RZuFtQ6e343nT8H3oxPRrNOCSeUwqSwmm8bJtOLkC6RyLm7W4GY1ayzxsaGNwnfVJygVsbUStloi9CpYLwrfJtwveLuDuKmBvb/8vuRYNoVPNwHTXhG8fXr23Wda0/V62zDEVmoEA0P4vUVqfdGMtl8OCTwX32YJ3JaXrTwSzWpn0lUyuRrZbI1MtloP3DWymSpuk7eQWGuh5uHvGiDY3hu1iQwWo1aRoRK2VN73ZNclNXsa6QVHkDl6Lun5c+qXuaTmTldfduOa7GHakimsoND5o2iljrXHkjn5HDJHz469Tpnc/KJPtXcHwcB2THU7GXc92cz2vR+vhfOouqdQNYupcnKsAXt/xg6Tt/eRsw+T4Umy4ZOkzI69Hw9tmpo3nVptOoGdgnVbIZXHzWVwcm24+VZSBRc3pxlkmXhsGM14R5cKtjKMrRUJa9Vo1jss1YP3EK4zRCo1QModGLHdJLTpemvJtH3hm6n4ZurLZr6nYWmd8MHbBj7hUAl/1zBef7m+8kiNoAqB7+Lb3KjDdiZT23tJp2sT/UvzqqznE/QPRSF71wBBsYQdLBIOlwiHS/tWFgFIuaTmziSzYC7pPUH76Lmk580mNWuq1slO1uQN08YZoqXrKjL5x6j19uD5p1FYtgQzAVbqGI1nN0fXC/V7wYQQBpZa7yB+/1bCYj9OsINMagOZ9K69z6mFR1JzTqRmFlIzJ1AzC/E48pDWAt4wuBaAI9rn730sZV+k1S6nEN5Czj6KMQHWOlRrc6nU5hI403Gy7bgtBVKt7aTbXFIFR0FZJrV9wdsSVErYyjBhtUjoVcHzouBt68HbHSTlDpByB18leGf3BW8zDZ9p+wXxPTPf0Yy3NRNzaTbreYSDRfy+Il5fCX+wFoXt2oHDNlhSbpVMxiOTrUXXmRqZTJV0xouum6xv24YhYblK2DdAsLMvahUplgkGh7F7gra/3zKHroM7o4f0vNmkj5xF6ogZpI+YSeqImaSPnIU7bRIufzq+Rhx9Tf/rTSr7HK1d38Y4gww/fwKZk8+hpclmox+IMpPC9AThuIbc9A6Yvm+nxaAWUt49hN+/FVvqxwl2knYfoSX98729n6HNU+NYas4JUcBmIVVzPKHpGfF1nut7EoAj2o6gxd5ER/ht8jwEQKmygN21yzD56aS7usl2Z+loUWgWGYlxDKmCS6oA0F6/jCz0ouBdqQSE5RJhtYitlgn9GtaP2kyMLeKaIVLus2Td+3HdwRF7vEObJ6AHf/82k/1nuvc+1tNQwduk07jdnbjdnYzUfbh3ZruviN9Xxh+qRmG7EhJ4hqCaYng4T+B0YF+xZnlI2q2RztTIZOuhO12f2c54pNM10mlvwuwcaRwHtyWP25InPeele1rsDdq7+6OgPVjEliqExRK1VeuoPvostvSypSoz6ah9ZN6sKGzPnUlqznRSs6aRmj2N1Iwe9WrHoIlnpi251l+Q77iOoNhKefcSWi44a0L3Rr+aSn1ZzNzEX0pY9hP6IbX+Cv7ATsJiP9T6ce0OspmNpNx9J/r54VRqzrF45hhqZgEex1IzCygHXbRxM9P4JzKsp+rNYqh0Nk7nbFrntJNuU3gWSYoNo9VNgmpIUA4JKyXC6jBhrUxY8+r93UUcijhmiJQ7WG81GRzxeKFt2dfLvXeWe2p95nsqgekmpL7M4ATo8Q4rVYLBYYKBUrQl+3CNoOQRVAMCzyEI0wQmR+AURgjc4Dr1YJ3xSGd8MvWQvSdspzMTK3S/3J7+7GC4TLCrj7B/CFssExbLhMMlbLGMLZVfcjIkAMbg9nSSmj39pSF77/VU3OndaiV5dZOpzcOjpetqsi33UNkyC9t9LrlTj9efPmTCs6ElKAfUBoYJhnYQlgZx/F24ZifZ9FZcd99JgqHN4pgqleqRDNYupuXIaRRmpBWgRSYYG1rCWohftQSVgLBSwlaHCWsVQq+G8avwkuA9UA/ewyMfz6YI6Kqv491TD9pT9q7pHV2mEO5dz7urIXe1DCvVqJ1ksN63PVwjKHuEVZ/AM4S+S0CawMkTmPxLVibZw3U8UqlavY3EJ5X2SKf2XHvRddonlZpYwdtai63WCIdKBH0DUdguV6OAXaoQFsvYSpWwVHlpvzaAY3CnTomC9YyppGZ0407vJjWtfj09unandk3GEyUnS5iu0drz72RyT1F84Rgyp17Y8NuBv1ZPbYquF81Jtg5Jjg0tfinAGxwiHNpFWBlioNJHsTaDOUctoGWWQrTIZLBvxtsSVHzCSjEK3l4Z6+1Zz7tSP7myhFNfyzvlDuK6r75baGA79m6iE+4fuk333t0so411ugjprG8rn/wElg18bKlCMFzGHygRDNWitbYrHmE1IPQMQeAS2AyBkyU0uRFnugEc45FOe6TSPumMH4XtlF9/bE/49kml/AmxTKD1fcJKDTtcJOgbivq1KxVsqUpYD922UsNWqtjqCPsCOAZ3SmcUrGf2kJreTWp6T3R/2pSo1aenE7enC6ejtVkmNCdBz7Sp0tb9b6SyzzC8+iTy51+E291x8M+b4Faui64Vpicv4xjSrSnSrV1AFwDX71l/XONCZNIwjqkvQwm0u0AWePXNo/bMegdVqFV8wmqJsFom9EpYzwe/BmEFwnIUvs0wKXcbGXeIlDuEMcHIx7UOAR1RsDadUcg2XQR07vdYdDswUQDfc38sZ8GNm8K0teK0tZI+wMZme5YDtMUy/lCZYLiCP+wRlj3CikfohYS+ISg5+DZFzckROC2Er7oDp63PevukUsHekL3n4qb2v19/nhtgxnH226RSuK0paC2QmjF15H9FEGJrHmG5gh0YJhwcjma1q7VoprtSJegbwN+yg0r9MUaapHUdnM523KlduD1dpHo6cbo7o/v1/nq3pysK392dOJ1tEyp8N9HMdEhr97+Tzj3O8PMnU7j4Ytz21qRLHBd7/kKjcwpkfxoXIjLW9u5mWbUEtZCgWsZWy1ivjK1VCH0fAj/azTKs4FCubydfJOUWcZxifUv5V88egW2tz263E5p2QhOtYx/SHgV000bIvsdC0x5d1y/WxHtulA3DKEyWKgTFCkFxT0+3T1itX3yL9SEIHELrEjpZQhNd7AFWZXKMt1/gDkilAtxUgOtGYdt1/fr9gNTe2z6uGyTahmLDEDw/ar0ZKu0L3ZUa1GrR16tSi2bCq1Wo+dFjI814AzgGp70Vp7MNt6sDZ0o7blc7bmc7zpQO3M42nK72KKB3tdWv26MZ8HhbT5p7ZrrQcS2Z/GMMP38ihYsmT5AGhSUZmcaFiIw145hoh9IMpHGBNAda3WSP0A8Ja5bAs3jVkKBawXoV8MvYWg3r+9iwvrGOrWIoY2wFx/TjuFtJO2Vcp4jjlF91a/m9r2XT9aDdUQ/aewJ4+8vCdxshrdHFtGBpJaSw9zHIjHiipnEcTD4H+Rxu98G/ZjYMoeYTVirRTG6xErWalD3CchS+rRcQ+kSXShTAfVKUTYbQaSEkc9C2GWMCXGf/IB7uDdpufebbcQNcJwrjjhtGH3Pqj7sBxtjDak8xjgPZDG42g9vRBkx/9a+HteD5WK/eZlIsEg7XT5ysevXw7UXjourhb92JfXFL9Dk179UDeJ3T1oJTD9tuR1sUyttb6pf67daX3W9vjT6vvRWTyxzyFvBN8b/bTP5+cm23Ulo/j9yyi3A7Jk+QBnhiQ3R98hHJ1iGNReNCRBqFk3JwUvuHjgyjCeE2tNgAQi8k8KBWCwlr0TKDtlaJrr2AMPAgCMDWIIwCuUMFY8q4zlZS7gu4ThnHKeE6lYO+LkQnaoa01C+tWBOF7pBW7Eseb6kH8Ogxy57ntWDJY02eMFvAyeaxHW2kRhnUbBBEobJai2Z8yzVsycOvt55YLyCs+dhaSBhYbGAIa4awWm9FMWmskyM06XoYH83rhlG4rgduNxXuC957Q/grA7njhPVLdNt1Qpz64y9/WWMMZNKYTBqnJQ+jaMfdM/Nt/SB6/0uV6GTKYoWwWotmumvevku1RrCzD3/LDvADbD28U/MO/iVIpXDaCjitBZyO1ihod7Titrcy7d/+YuRPGcVXtqE56V4KXVdT2zUFd8H5pKZ2JV3SuHvkxehaoUn2p3EhIhOdcQzGASe950/3e2bDR7+udhhYrBcSeuD7lpoXRFvM+zWsV4t2vPR9wiDAhkE9lHtgPUzoYUwVQxWHKo6zjZRTxXEq9UsZx4wioO1fj80TksdSvzaFeuguEFLY9zh5bLZAmMtjO/Z7jDyWHCFZrMlhye53yUUfC9PYwK3P8NbbL/b0f9d8wko9iHsB1g+ir09gsaHB+hZrHULrEFgXz6SwJk1ocoQmhSV9SEsrGkKME0QBux62HTfEcW09uIf7Lu5L7+/5HONYHBNinBAna3HyIaYnek7KWIwTjjirbq2FIPo34geEXgCVCmG5GvV872k58f19gdvzo5N1az7B7gH87bvAi8bFC1PP++DRO+/+/sv/jRM8TFtaZ/wAggAvOJOW4+clXVAiPnRu0hVII9K4EBGJNsrCdXH3tlKnYMTtZA7MhhZriYKnD0Fg8T0IPZ/Qr0HoRcE8iAK6DUIIg6jHOgyjHg7rY6wPeBjrYfAwpoYxQzjOLlKmiuPUMKaG41RxTOWgbS2v/g+HMJfB5rLY9n1B25rs3jCOyRLuefwlgTxTv6SxoUvop7CBS+g7hJ4hrGUIqmn8ahq/lib00oSeS1gzBL6L9R1C3yUMnOh26GI9B2tdrHXwcbAmVb9ksMYlJIUl9RrWQLcYE0ah2+wJ5FHQ3nN7730T4rSEmLb9QroJ66F9z3Oi42FCHAIoF+EH31kw0iuPW5g2xjjAfwCnAFXgY8D5wG8Cj1hrf7f+vB8Av22tHXll+v1cekqJTMsGhp9fRMubTo2v+AbnTpwTXmUcaVyIiIwd45jo7DPX5aWnuKWA13bS4552FhuEhAHYwOL50bX1A2zgQ+ARBjX2NFfbIIAgJAxDCENsaMGGYKMZdmMDwMdQD/F7bhsfhxrGFDHGx3U8HBOFesd4GKeGMR6OqZ/Fboj+GDAGG8NZ6+wL6TZdv05hbQpsijB0CYMsvl8gDAoEXo7AzxEEOQI/iw3SBH6GMMgQ7rkOUoRBet/tMIOtX4dhqj5LnyKwaTxbf12bJrRRHaGth/iRzy18ibm55SOutTyeM9NXADlr7dnGmLOALwGdwDnADcaYrvrtu0cTpAH+5M19eH0dZE85C5OdvNv/PVb/c/7iI5OtQxqLxoWIyMSwp52F9MuDOoxJih2BDe1+Id5io0we3Q9t9FgQYG2A9UOw9QBvQ2wQYEIfa8OoNcaG2JBoWTxbn4m3YX2ZvKB+UFsP9gEQRrcJ6tchYDEEQIDrlnBTQ2SyAcYE9RniAMO++8ZEn2eMHz1GACbY90vAoXwtbL1HPsjUQ3h0sWGaMEzXb7s8N/wHIzZ4j2eYXgbcHBVt7zfGLAWeIDoLIUX0lfwI8N7RHrAtH1JedSztZ7WDN3TwT2hSj62PNq1ZPKt0kGfKZKJxISIir8bULzj1y0GlGM/YaC1g97XXEFqsNfUZeOrhHcL9bltrIdzzOfUZ+72/Jdh6yA/2hv76k6PPsxacEGvB2LC+XHb9eACBxxHnX1+Gj76i1nFbZ9oY85/AT6y1P6/f3wC8H/hD4BaiUP0iURvIXOAr1tpVIxzn48DHAXKOOWV6B7uDqAFJJhhrKRiDkt4EpPduYtP7N7Hp/Zu49N5NXAaMhe0bd9lX9BWP58z0INC2333HWrsCWGGM6QC+AfwSeCPwOeCrwAdefhBr7beAbwEYY1au322Xxl24xMMYs9JavX8Tkd67iU3v38Sm92/i0nvXnMbzFKV7gDcB1Humn9zvY58G/hEoAAHRjPrkWixaRERERCac8ZyZvgG4xBhzL1Gbzm8AGGPmAZ3W2sfrK34cAdwEfHYcaxMREREROWTjFqattSHw2yM8vh74nf2e8/ZDOOy3xqQ4SYrev4lL793EpvdvYtP7N3HpvWtC43YCooiIiIhIs9G2DiIiIiIih0lhWkRERETkME3IMG2McYwx3zDG3GeMucMYM+Je6dJYjDGP1N+vO4wx3zXGnGWMecAYc48x5q+Srk9eyRhzpjHmjvrtBcaYFcaYu40xX6+fMIwx5q+MMQ8aY+41xpyRaMHyEi97/041xmze73vwvfXH9f41GGNM2hhzdf177UFjzNv0/TcxvMp7p++9Jjeeq3mMpSt45dbklydbkhyIMSZH1KN/wX6PPQa8E1gL3GiMOdVa+2gyFcrLGWP+DPgQUKw/9C/AZ621dxhjvgFcbox5ETgfOJNos6WfAKcnUa+81Ajv32nAv1hrv7Tfc5ag968RfRDYZa39kDFmCvBY/aLvv8Y30nv3efS919Qm5Mw0L9uaHNAC6I3vFKBgjLnFGPMrY8zrgKy19gUbnQX7C+DiZEuUl3kBeMd+908D7qzf/jnR+7UMuMVGNgApY8zU8S1TXsVI79+bjTF3GWO+Y4xpQ+9fo/ox0eZlEC0l66Pvv4ni1d47fe81sYkaptuBgf3uB8aYiTrLPlmUgC8ClxItkfjd+mN7DAEdCdQlr8Ja+xPA2+8hY/ct/7Pn/Xr596LexwYxwvv3IPBJa+3riP4a9Ffo/WtI1tpha+1QPXRdR7Tvgr7/JoBXee/0vdfkJmqYHmlrcj+pYmRUVgPfr/8Wvproh8iU/T7eBvQnUZiMWrjf7T3v18u/F/U+Nq4brLUP77kNnIrev4ZljJkL3A5cba39Afr+mzBGeO/0vdfkJmqYPtDW5NKYPkLU244xZhbR1vFFY8zRxhhDNGN9d4L1ycE9aoy5oH77jUTv1z3ApfWTgo8g+sW2N6H65MB+sd9JThcBD6P3ryEZY6YDtwCfstb+V/1hff9NAK/y3ul7r8lN1NaIEbcml4b2HeAqY8wKwBKF6xC4BnCJesceSLA+Obj/B3zbGJMBngWus9YGxpi7gfuIfjn/vSQLlAP6HeBfjTEesA34uLV2UO9fQ/oM0AV8zhizp//2j4Cv6fuv4Y303v0J8GV97zUv7YAoIiIiInKYJmqbh4iIiIhI4hSmRUREREQOk8K0iIiIiMhhUpgWERERETlMCtMiIiIiIodJYVpEpAkZY3LGmPVJ1yEi0uwUpkVEREREDtNE3bRFRERexhjTSrQRUhewpv7Y+cBfEU2etALvBy4AjrHWftIY4wKPAacD1wIdRDuU/oW19pZx/ieIiEw4mpkWEWkevw08Za19HfDN+mMnAh+01l4AXA+8G/ghcEU9SF8G3A4cDfQAbwXehyZbRERGRT8sRUSax7HAjQDW2gfq2xdvJtqGehiYDdxjrR0yxtwJXAr8BvB5a+3TxphvEgXtNPC1RP4FIiITjMK0iEjzeAY4G1hujDmVKBR/Gzi6HqD/GzD1534b+BTQY619whhzEtBmrX2zMWYmcC/ws/H/J4iITCwK0yIizeMbwPeMMSuA54AqUWvH3caYIrAdmAV7Z64XAP9e/9zngb8yxryHqAXwL8e7eBGRichYa5OuQURExpkxxgHuAS611g4mXY+IyESlExBFRCYZY8xRwCPAjxSkRUReG81Mi4iIiIgcJs1Mi4iIiIgcJoVpEREREZHDpDAtIiIiInKYFKZFRERERA6TwrSIiIiIyGH6/wH89j/LLrYiWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='days', ylabel='percent of population'>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference simulation visualizations\n",
    "\n",
    "We can also visualize the results of another simulation(s) as a reference for comparison of our main simulation.\n",
    "\n",
    "Here we simulate a model where no distancing or testing takes place, so that we can compare the effects of these interventions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 299.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model = SEIRSModel(beta=BETA, sigma=SIGMA, gamma=GAMMA, mu_I=MU_I, initI=10000, initN=1000000) \n",
    "ref_model.run(T=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize our main simulation together with this reference simulation by passing the model object of the reference simulation to the appropriate figure function argument (note: a second reference simulation could also be visualized by passing it to the ```dasheQ_reference_results``` argument):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/seirsplus/models.py:406: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,.0%}'.format(y) for y in ax.get_yticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='days', ylabel='percent of population'>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.25, shaded_reference_results=ref_model)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
